A Multidimensional TDOA Association Algorithm for Joint Multitarget Localization and Multisensor Synchronization

This article considers the problem of multitarget localization using time difference of arrival (TDOA) measurements at multiple sensors with misaligned clocks in the presence of data association uncertainty. Sensor synchronization and data association are two essential steps in target localization and tracking to align sensor clocks and to associate measurements from different sensors. In practice, sensor synchronization errors can adversely affect data association performance and vice versa. Although these two processes affect each other, they are usually addressed separately. We propose a novel joint multidimensional association algorithm for multisensor synchronization (JMDA4MS), which performs data association and yields sensor clock offset and target position estimates simultaneously using TDOA measurements. Considering the observability of the unknown parameters, the joint multiframe multidimensional association algorithm is developed as a multiframe extension of the JMDA4MS algorithm. A gating method and a multidimensional plus sequential two-dimensional association approach are utilized to improve the efficiency of the proposed algorithms. The Cramér-Rao lower bound for the proposed maximum likelihood estimator is derived as a performance benchmark. Computer simulations are carried out to evaluate the performance of the proposed algorithms on different scenarios.